Library/Prospecting & Pipeline Creation/Lead Sourcing & List Building/Create an Account Prioritization Matrix
TestedProspecting & Pipeline CreationQuick Win (under 10 min)
About this prompt
This prompt builds an account prioritization matrix by scoring a target list against ICP fit, buying signal strength, engagement history, and competitive risk — producing a ranked list with justification for the top tier. Use it when you have more accounts than you can actively work and need a defensible way to allocate time.Random prioritization leads to reps spending equal time on accounts with very different conversion probabilities. This prompt takes your [account list with available data], [ICP criteria], [signal events], and [competitive context] and produces a scoring matrix covering ICP fit dimensions, signal strength, existing relationship depth, competitive risk, and estimated deal size potential — each with a weight assigned and a score for each account. The output includes a ranked list with the rationale for the top-tier accounts and explicit flags for accounts that shouldn't be in the territory at all. Use this over a gut-feel prioritization when you're accountable for territory performance and need to show your manager why you're spending time where you are. Tip: revisit the matrix monthly rather than setting it once — accounts that scored low on signals in month one may score much higher after a funding event or executive hire.The prompt
Create an account prioritization matrix for my target list:
Criteria to consider:
- Fit with ICP
- Buying signals present
- Competitive situation
- Relationship/inroads
- Deal size potential
- Sales
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Variables to replace
How to run it
- Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
- Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
- Make it yours — run it, keep what's strong, and tighten your inputs on the next pass. The prompt improves with your context.
What good output looks like
Make it yours
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